LAUSR

Revealing human internal control mechanisms during environmental interaction remains paramount and helpful in solving issues related to human-robot interaction. Muscle reflexes, which can directly and rapidly modify the dynamic behavior of joints, are the fundamental control loops of the Central Nervous System. This study investigates the calf muscle reflex control in the “ankle strategy” for human push-recovery movement. A time-increasing searching method is proposed to evaluate the feasibility of the reflex model in terms of predicting real muscle activations. Constraints with physiological implications are imposed to find the appropriate reflex gains. The experimental results show that the reflex model fits over 90% of the forepart of muscle activation. With the increasing of time, the Variance Accounted For (VAF) values drop to below 80% and reflex gains lose the physiology meaning. By dividing the muscle activation into two parts, the reflex formula is still workable for the rest part, with different gains and lower VAF values. This result may indicate that reflex control could more likely dominate the forepart of the push-recovery motion and an analogous control mechanism is still feasible for the rest of the motion part, with different gains. The proposed method provides an alternative way to obtain the human internal control mechanism desired for human-robot interaction task.